Download e-book for iPad: Quantum Interaction: 6th International Symposium, QI 2012, by Kirsty Kitto, Fabio Boschetti, Peter Bruza (auth.), Jerome

By Kirsty Kitto, Fabio Boschetti, Peter Bruza (auth.), Jerome R. Busemeyer, François Dubois, Ariane Lambert-Mogiliansky, Massimo Melucci (eds.)

ISBN-10: 3642356583

ISBN-13: 9783642356582

ISBN-10: 3642356591

ISBN-13: 9783642356599

This e-book constitutes the refereed lawsuits of the sixth overseas Symposium on Quantum interplay, QI 2012, held in Paris in June 2012. The 21 revised complete papers awarded have been conscientiously reviewed and chosen from 32 submissions. The papers hide quite a few issues on quantum interaction.

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Extra resources for Quantum Interaction: 6th International Symposium, QI 2012, Paris, France, June 27-29, 2012, Revised Selected Papers

Sample text

This concludes our summary of the operations common to all vectors. We will now proceed to describe the three implementations available so far, for vectors using real, complex, and binary numbers as coordinates. 3 Real Vectors The use of real vectors for representation in empirical learning is by far the most common choice of ground-field to date. In the Semantic Vectors package, real vectors are implemented using single-precision, 4-byte floating point numbers. Randomly-generated elemental vectors are sparse ternary vectors: ternary, meaning that they use only values from the set {−1, 0, 1}, and sparse, meaning that most values are left as zero.

Consider the data in Tab. 1. It is based on data obtained by asking participants to estimate how typical various exemplars are of the concepts Furniture, Household Appliances, and Furniture and Household Appliances [14]. Table 1. Interference data for concepts A=Furniture and B=Household Appliances. The probability of a participant choosing exemplar k as an example of Furniture or Household Appliances is given by μ(A)k or (μ(B)k , respectively. The probability of a participant choosing a particular exemplar k as an example of Furniture and Household k .

Cohen In addition to the fundamental Vector Symbolic operations of superposition (or bundling) and binding [12], we have implemented binary approximations of orthogonalization, and quantum disjunction [25]. Orthogonality in binary vector space is defined by a HD of a half of the dimensionality of the space. Give two similar vectors, A and B, A can be rendered almost-orthogonal to B by introducing random noise in dimensions that these vectors have in common. Conversely, two vectors with a HD of more than half the dimensionality can be rendered orthogonal by randomly selecting dimensions in which these vectors differ, and changing them accordingly.

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Quantum Interaction: 6th International Symposium, QI 2012, Paris, France, June 27-29, 2012, Revised Selected Papers by Kirsty Kitto, Fabio Boschetti, Peter Bruza (auth.), Jerome R. Busemeyer, François Dubois, Ariane Lambert-Mogiliansky, Massimo Melucci (eds.)


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